6 papers
Latent Order Bandits
Emil Carlsson, Newton Mwai, Fredrik D. Johansson
Bandit algorithms solve diverse sequential decision-making problems, but are often too sample-inefficient for from-scratch personalization. To substantially reduce exploration time…
Latent Preference Bandits
Newton Mwai, Emil Carlsson, Fredrik D. Johansson
Bandit algorithms are guaranteed to solve diverse sequential decision-making problems, provided that a sufficient exploration budget is available. However, learning from scratch is…
Identifiable Latent Bandits: Leveraging observational data for personalized decision-making
Ahmet Zahid Balcıoğlu, Newton Mwai, Emil Carlsson +1
Sequential decision-making algorithms such as multi-armed bandits can find optimal personalized decisions, but are notoriously sample-hungry. In personalized medicine, for example,…
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
Fredrik D. Johansson
Evaluating observational estimators of causal effects demands information that is rarely available: unconfounded interventions and outcomes from the population of interest, created…
Active Preference Learning for Ordering Items In- and Out-of-sample
Herman Bergström, Emil Carlsson, Devdatt Dubhashi +1
Learning an ordering of items based on pairwise comparisons is useful when items are difficult to rate consistently on an absolute scale, for example, when annotators have to make…
Pure Exploration in Bandits with Linear Constraints
Emil Carlsson, Debabrota Basu, Fredrik D. Johansson +1
We address the problem of identifying the optimal policy with a fixed confidence level in a multi-armed bandit setup, when \emph{the arms are subject to linear constraints}. Unlike…